Overview

Dataset statistics

Number of variables21
Number of observations3168
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory519.9 KiB
Average record size in memory168.0 B

Variable types

Numeric20
Categorical1

Warnings

Dataset has 2 (0.1%) duplicate rowsDuplicates
meanfreq is highly correlated with sd and 11 other fieldsHigh correlation
sd is highly correlated with meanfreq and 7 other fieldsHigh correlation
median is highly correlated with meanfreq and 7 other fieldsHigh correlation
Q25 is highly correlated with meanfreq and 8 other fieldsHigh correlation
Q75 is highly correlated with meanfreq and 2 other fieldsHigh correlation
IQR is highly correlated with meanfreq and 6 other fieldsHigh correlation
skew is highly correlated with kurtHigh correlation
kurt is highly correlated with skewHigh correlation
sp.ent is highly correlated with meanfreq and 7 other fieldsHigh correlation
sfm is highly correlated with meanfreq and 6 other fieldsHigh correlation
mode is highly correlated with meanfreq and 4 other fieldsHigh correlation
centroid is highly correlated with meanfreq and 11 other fieldsHigh correlation
meanfun is highly correlated with Q25 and 2 other fieldsHigh correlation
meandom is highly correlated with meanfreq and 3 other fieldsHigh correlation
maxdom is highly correlated with meanfreq and 3 other fieldsHigh correlation
dfrange is highly correlated with meanfreq and 3 other fieldsHigh correlation
meanfreq is highly correlated with sd and 11 other fieldsHigh correlation
sd is highly correlated with meanfreq and 7 other fieldsHigh correlation
median is highly correlated with meanfreq and 7 other fieldsHigh correlation
Q25 is highly correlated with meanfreq and 8 other fieldsHigh correlation
Q75 is highly correlated with meanfreq and 3 other fieldsHigh correlation
IQR is highly correlated with meanfreq and 6 other fieldsHigh correlation
skew is highly correlated with kurtHigh correlation
kurt is highly correlated with skewHigh correlation
sp.ent is highly correlated with meanfreq and 7 other fieldsHigh correlation
sfm is highly correlated with meanfreq and 6 other fieldsHigh correlation
mode is highly correlated with meanfreq and 4 other fieldsHigh correlation
centroid is highly correlated with meanfreq and 11 other fieldsHigh correlation
meanfun is highly correlated with Q25 and 2 other fieldsHigh correlation
minfun is highly correlated with meandomHigh correlation
meandom is highly correlated with meanfreq and 4 other fieldsHigh correlation
maxdom is highly correlated with meanfreq and 4 other fieldsHigh correlation
dfrange is highly correlated with meanfreq and 3 other fieldsHigh correlation
meanfreq is highly correlated with sd and 5 other fieldsHigh correlation
sd is highly correlated with meanfreq and 5 other fieldsHigh correlation
median is highly correlated with meanfreq and 4 other fieldsHigh correlation
Q25 is highly correlated with meanfreq and 7 other fieldsHigh correlation
Q75 is highly correlated with meanfreq and 2 other fieldsHigh correlation
IQR is highly correlated with sd and 2 other fieldsHigh correlation
skew is highly correlated with kurtHigh correlation
kurt is highly correlated with skewHigh correlation
sp.ent is highly correlated with sd and 3 other fieldsHigh correlation
sfm is highly correlated with meanfreq and 4 other fieldsHigh correlation
mode is highly correlated with medianHigh correlation
centroid is highly correlated with meanfreq and 5 other fieldsHigh correlation
meanfun is highly correlated with Q25High correlation
meandom is highly correlated with maxdom and 1 other fieldsHigh correlation
maxdom is highly correlated with meandom and 1 other fieldsHigh correlation
dfrange is highly correlated with meandom and 1 other fieldsHigh correlation
meanfun is highly correlated with sp.ent and 10 other fieldsHigh correlation
sp.ent is highly correlated with meanfun and 9 other fieldsHigh correlation
maxdom is highly correlated with modindx and 7 other fieldsHigh correlation
Q75 is highly correlated with centroid and 4 other fieldsHigh correlation
IQR is highly correlated with meanfun and 11 other fieldsHigh correlation
modindx is highly correlated with maxdom and 2 other fieldsHigh correlation
centroid is highly correlated with meanfun and 13 other fieldsHigh correlation
minfun is highly correlated with meanfun and 4 other fieldsHigh correlation
dfrange is highly correlated with maxdom and 7 other fieldsHigh correlation
Q25 is highly correlated with meanfun and 16 other fieldsHigh correlation
mode is highly correlated with meanfun and 16 other fieldsHigh correlation
meandom is highly correlated with maxdom and 7 other fieldsHigh correlation
label is highly correlated with meanfun and 8 other fieldsHigh correlation
sfm is highly correlated with meanfun and 9 other fieldsHigh correlation
kurt is highly correlated with IQR and 4 other fieldsHigh correlation
sd is highly correlated with meanfun and 12 other fieldsHigh correlation
meanfreq is highly correlated with meanfun and 13 other fieldsHigh correlation
median is highly correlated with meanfun and 9 other fieldsHigh correlation
mindom is highly correlated with Q25 and 1 other fieldsHigh correlation
skew is highly correlated with IQR and 4 other fieldsHigh correlation
label is uniformly distributed Uniform
mode has 236 (7.4%) zeros Zeros
dfrange has 65 (2.1%) zeros Zeros
modindx has 65 (2.1%) zeros Zeros

Reproduction

Analysis started2022-04-13 18:56:05.685975
Analysis finished2022-04-13 18:56:54.116535
Duration48.43 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

meanfreq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3166
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1809066104
Minimum0.03936334258
Maximum0.2511237587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:54.197544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.03936334258
5-th percentile0.1259677733
Q10.1636621363
median0.1848384094
Q30.1991460509
95-th percentile0.2291036805
Maximum0.2511237587
Range0.2117604161
Interquartile range (IQR)0.03548391458

Descriptive statistics

Standard deviation0.0299178379
Coefficient of variation (CV)0.1653772509
Kurtosis0.805160543
Mean0.1809066104
Median Absolute Deviation (MAD)0.01738691301
Skewness-0.617495272
Sum573.1121417
Variance0.0008950770245
MonotonicityNot monotonic
2022-04-13T19:56:54.329532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21218991492
 
0.1%
0.21373237172
 
0.1%
0.22890274791
 
< 0.1%
0.10039941711
 
< 0.1%
0.16015517991
 
< 0.1%
0.23374421481
 
< 0.1%
0.19097060781
 
< 0.1%
0.15660550981
 
< 0.1%
0.13285463091
 
< 0.1%
0.17772273781
 
< 0.1%
Other values (3156)3156
99.6%
ValueCountFrequency (%)
0.039363342581
< 0.1%
0.048254075191
< 0.1%
0.059645486741
< 0.1%
0.059780984961
< 0.1%
0.062182311861
< 0.1%
0.066008740391
< 0.1%
0.074675083071
< 0.1%
0.07570098591
< 0.1%
0.07731550271
< 0.1%
0.078847187171
< 0.1%
ValueCountFrequency (%)
0.25112375871
< 0.1%
0.24963659291
< 0.1%
0.24704068411
< 0.1%
0.24435644691
< 0.1%
0.24352803661
< 0.1%
0.24327214161
< 0.1%
0.24326630321
< 0.1%
0.24263912671
< 0.1%
0.24208161481
< 0.1%
0.24174181061
< 0.1%

sd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3166
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05712596491
Minimum0.01836324244
Maximum0.1152732467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:54.480536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01836324244
5-th percentile0.03161699632
Q10.04195354558
median0.05915511913
Q30.06702042292
95-th percentile0.08548695077
Maximum0.1152732467
Range0.0969100043
Interquartile range (IQR)0.02506687734

Descriptive statistics

Standard deviation0.01665224708
Coefficient of variation (CV)0.2915004956
Kurtosis-0.5217889483
Mean0.05712596491
Median Absolute Deviation (MAD)0.01247739916
Skewness0.1369163179
Sum180.9750568
Variance0.0002772973329
MonotonicityNot monotonic
2022-04-13T19:56:54.898577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.043190430892
 
0.1%
0.057704738822
 
0.1%
0.037321151871
 
< 0.1%
0.037140777271
 
< 0.1%
0.043050491211
 
< 0.1%
0.033296919331
 
< 0.1%
0.032307871691
 
< 0.1%
0.024562790291
 
< 0.1%
0.080241702391
 
< 0.1%
0.03740623711
 
< 0.1%
Other values (3156)3156
99.6%
ValueCountFrequency (%)
0.018363242441
< 0.1%
0.021781990641
< 0.1%
0.024001665021
< 0.1%
0.024268932091
< 0.1%
0.024562790291
< 0.1%
0.025068265571
< 0.1%
0.025500205741
< 0.1%
0.025519586131
< 0.1%
0.02597302931
< 0.1%
0.025998921441
< 0.1%
ValueCountFrequency (%)
0.11527324671
< 0.1%
0.11450803821
< 0.1%
0.11264911881
< 0.1%
0.11126049221
< 0.1%
0.11125696931
< 0.1%
0.11026080011
< 0.1%
0.10937777951
< 0.1%
0.10904435021
< 0.1%
0.10786176191
< 0.1%
0.10754905721
< 0.1%

median
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3077
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1856206765
Minimum0.01097457627
Maximum0.2612244898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:55.038580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01097457627
5-th percentile0.1163549305
Q10.1695925234
median0.1900323792
Q30.2106181268
95-th percentile0.2358259823
Maximum0.2612244898
Range0.2502499135
Interquartile range (IQR)0.04102560347

Descriptive statistics

Standard deviation0.03636014631
Coefficient of variation (CV)0.1958841386
Kurtosis1.629500928
Mean0.1856206765
Median Absolute Deviation (MAD)0.02057813232
Skewness-1.012784663
Sum588.0463031
Variance0.00132206024
MonotonicityNot monotonic
2022-04-13T19:56:55.257535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18666666676
 
0.2%
0.224
 
0.1%
0.17203155823
 
0.1%
0.17923
 
0.1%
0.18344827593
 
0.1%
0.20413255363
 
0.1%
0.21212121213
 
0.1%
0.18598540152
 
0.1%
0.19092046472
 
0.1%
0.16603194892
 
0.1%
Other values (3067)3137
99.0%
ValueCountFrequency (%)
0.010974576271
< 0.1%
0.013587521661
< 0.1%
0.015790309771
< 0.1%
0.026994680851
< 0.1%
0.029361296471
< 0.1%
0.029364406781
< 0.1%
0.032026913371
< 0.1%
0.03511371021
< 0.1%
0.036718458671
< 0.1%
0.040228734811
< 0.1%
ValueCountFrequency (%)
0.26122448981
< 0.1%
0.26054054051
< 0.1%
0.25741704951
< 0.1%
0.25698355971
< 0.1%
0.25663125951
< 0.1%
0.25640194491
< 0.1%
0.25513920241
< 0.1%
0.25503063311
< 0.1%
0.25472417251
< 0.1%
0.25463247861
< 0.1%

Q25
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3103
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1404555905
Minimum0.0002287581699
Maximum0.2473469388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:55.436533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0002287581699
5-th percentile0.04358040895
Q10.1110865126
median0.1402864183
Q30.1759387716
95-th percentile0.2152441287
Maximum0.2473469388
Range0.2471181806
Interquartile range (IQR)0.06485225897

Descriptive statistics

Standard deviation0.04867971586
Coefficient of variation (CV)0.346584395
Kurtosis0.01833354583
Mean0.1404555905
Median Absolute Deviation (MAD)0.03264112353
Skewness-0.4908766849
Sum444.9633107
Variance0.002369714736
MonotonicityNot monotonic
2022-04-13T19:56:55.577533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.146
 
0.2%
0.17161290323
 
0.1%
0.20363636363
 
0.1%
0.15906696762
 
0.1%
0.16869029282
 
0.1%
0.14808888892
 
0.1%
0.21636363642
 
0.1%
0.18895705522
 
0.1%
0.1031275722
 
0.1%
0.16134529152
 
0.1%
Other values (3093)3142
99.2%
ValueCountFrequency (%)
0.00022875816991
< 0.1%
0.00023549201011
< 0.1%
0.00023952095811
< 0.1%
0.00025022341381
< 0.1%
0.0002669208772
0.1%
0.00027613412231
< 0.1%
0.00028600612871
< 0.1%
0.00034825870651
< 0.1%
0.0015498154981
< 0.1%
0.0017796610171
< 0.1%
ValueCountFrequency (%)
0.24734693881
< 0.1%
0.24212353241
< 0.1%
0.2407351941
< 0.1%
0.24054162491
< 0.1%
0.23945945951
< 0.1%
0.23858297081
< 0.1%
0.23793626711
< 0.1%
0.23616869191
< 0.1%
0.2361466751
< 0.1%
0.2359212881
< 0.1%

Q75
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3034
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2247649614
Minimum0.04294627383
Maximum0.2734693878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:55.707577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.04294627383
5-th percentile0.1874137339
Q10.2087466155
median0.2256842149
Q30.2436604825
95-th percentile0.2576779694
Maximum0.2734693878
Range0.2305231139
Interquartile range (IQR)0.03491386695

Descriptive statistics

Standard deviation0.02363927828
Coefficient of variation (CV)0.1051733248
Kurtosis2.981810301
Mean0.2247649614
Median Absolute Deviation (MAD)0.01742425072
Skewness-0.9003108148
Sum712.0553978
Variance0.0005588154777
MonotonicityNot monotonic
2022-04-13T19:56:55.832536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.245
 
0.2%
0.24181818185
 
0.2%
0.2454
 
0.1%
0.22909090914
 
0.1%
0.24888888894
 
0.1%
0.23333333334
 
0.1%
0.24137931034
 
0.1%
0.20473118283
 
0.1%
0.25492537313
 
0.1%
0.24778082193
 
0.1%
Other values (3024)3129
98.8%
ValueCountFrequency (%)
0.042946273831
< 0.1%
0.058268467041
< 0.1%
0.075957446811
< 0.1%
0.090193439871
< 0.1%
0.092666190141
< 0.1%
0.11745762711
< 0.1%
0.12763666951
< 0.1%
0.12826666671
< 0.1%
0.12965677721
< 0.1%
0.13031914891
< 0.1%
ValueCountFrequency (%)
0.27346938781
< 0.1%
0.26985172981
< 0.1%
0.26893732971
< 0.1%
0.26892405061
< 0.1%
0.26879199431
< 0.1%
0.26864864861
< 0.1%
0.26852807281
< 0.1%
0.26851282051
< 0.1%
0.26799387441
< 0.1%
0.26771159871
< 0.1%

IQR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3073
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08430937093
Minimum0.01455773126
Maximum0.2522252011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:55.957536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01455773126
5-th percentile0.0254866894
Q10.04255973444
median0.09427995392
Q30.1141750866
95-th percentile0.1563189556
Maximum0.2522252011
Range0.2376674698
Interquartile range (IQR)0.07161535212

Descriptive statistics

Standard deviation0.04278305438
Coefficient of variation (CV)0.5074531326
Kurtosis-0.448160298
Mean0.08430937093
Median Absolute Deviation (MAD)0.03265519384
Skewness0.2954323558
Sum267.0920871
Variance0.001830389742
MonotonicityNot monotonic
2022-04-13T19:56:56.082536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0354
 
0.1%
0.043076923084
 
0.1%
0.1054
 
0.1%
0.023333333333
 
0.1%
0.024888888893
 
0.1%
0.1058445043
 
0.1%
0.046666666673
 
0.1%
0.1022
 
0.1%
0.097391304352
 
0.1%
0.094357366772
 
0.1%
Other values (3063)3138
99.1%
ValueCountFrequency (%)
0.014557731261
< 0.1%
0.014922480621
< 0.1%
0.015111111111
< 0.1%
0.015491009681
< 0.1%
0.016585365851
< 0.1%
0.016586741891
< 0.1%
0.016733067731
< 0.1%
0.017082228121
< 0.1%
0.017409326421
< 0.1%
0.017428958051
< 0.1%
ValueCountFrequency (%)
0.25222520111
< 0.1%
0.24877025741
< 0.1%
0.24819165381
< 0.1%
0.24596527071
< 0.1%
0.2453002861
< 0.1%
0.24099502491
< 0.1%
0.24067283431
< 0.1%
0.239745831
< 0.1%
0.23630718951
< 0.1%
0.23195266271
< 0.1%

skew
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3166
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.14016752
Minimum0.1417354241
Maximum34.72545327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:56.210538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1417354241
5-th percentile1.122956172
Q11.649568695
median2.197100657
Q32.931694047
95-th percentile6.918370952
Maximum34.72545327
Range34.58371784
Interquartile range (IQR)1.282125352

Descriptive statistics

Standard deviation4.240528713
Coefficient of variation (CV)1.350414806
Kurtosis25.36344634
Mean3.14016752
Median Absolute Deviation (MAD)0.610138984
Skewness4.933314185
Sum9948.050704
Variance17.98208377
MonotonicityNot monotonic
2022-04-13T19:56:56.339580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8625728092
 
0.1%
2.1135982782
 
0.1%
2.1638479441
 
< 0.1%
1.5223540931
 
< 0.1%
2.0525506571
 
< 0.1%
2.5592147531
 
< 0.1%
1.4305035851
 
< 0.1%
2.212285251
 
< 0.1%
3.0309837941
 
< 0.1%
1.4080928681
 
< 0.1%
Other values (3156)3156
99.6%
ValueCountFrequency (%)
0.14173542411
< 0.1%
0.28502028531
< 0.1%
0.32603303361
< 0.1%
0.5295838031
< 0.1%
0.54874270531
< 0.1%
0.58978746391
< 0.1%
0.6080514721
< 0.1%
0.60837104381
< 0.1%
0.63019105881
< 0.1%
0.6542092261
< 0.1%
ValueCountFrequency (%)
34.725453271
< 0.1%
34.537487561
< 0.1%
33.566337531
< 0.1%
33.167300361
< 0.1%
32.350739271
< 0.1%
32.20286921
< 0.1%
31.951458841
< 0.1%
31.723464421
< 0.1%
31.355067591
< 0.1%
30.822755241
< 0.1%

kurt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3166
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.56846079
Minimum2.068455491
Maximum1309.612887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:56.464531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.068455491
5-th percentile3.755363316
Q15.669546856
median8.318463289
Q313.64890532
95-th percentile75.16913085
Maximum1309.612887
Range1307.544432
Interquartile range (IQR)7.979358464

Descriptive statistics

Standard deviation134.9286612
Coefficient of variation (CV)3.689755005
Kurtosis35.93212929
Mean36.56846079
Median Absolute Deviation (MAD)3.30452894
Skewness5.872586435
Sum115848.8838
Variance18205.74362
MonotonicityNot monotonic
2022-04-13T19:56:56.594579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1097902862
 
0.1%
7.8909269852
 
0.1%
3.8204725061
 
< 0.1%
7.4065554571
 
< 0.1%
11.50905831
 
< 0.1%
45.895247821
 
< 0.1%
810.44920341
 
< 0.1%
21.965579691
 
< 0.1%
9.118889121
 
< 0.1%
15.232228751
 
< 0.1%
Other values (3156)3156
99.6%
ValueCountFrequency (%)
2.0684554911
< 0.1%
2.2096727721
< 0.1%
2.2694322231
< 0.1%
2.2933681
< 0.1%
2.462561321
< 0.1%
2.5039544691
< 0.1%
2.5278803981
< 0.1%
2.5722301551
< 0.1%
2.6033621011
< 0.1%
2.6272162381
< 0.1%
ValueCountFrequency (%)
1309.6128871
< 0.1%
1271.3536281
< 0.1%
1202.6845521
< 0.1%
1193.4340661
< 0.1%
1128.5347821
< 0.1%
1122.2190231
< 0.1%
1116.0366221
< 0.1%
1087.767271
< 0.1%
1077.2068551
< 0.1%
1045.5285021
< 0.1%

sp.ent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3166
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8951270643
Minimum0.7386506862
Maximum0.981996589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:56.729531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.7386506862
5-th percentile0.8167506621
Q10.8618109811
median0.9017668303
Q30.9287134567
95-th percentile0.962986275
Maximum0.981996589
Range0.2433459027
Interquartile range (IQR)0.06690247559

Descriptive statistics

Standard deviation0.0449795184
Coefficient of variation (CV)0.05024931117
Kurtosis-0.423924736
Mean0.8951270643
Median Absolute Deviation (MAD)0.03209117622
Skewness-0.4309339825
Sum2835.76254
Variance0.002023157075
MonotonicityNot monotonic
2022-04-13T19:56:56.873532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87766886892
 
0.1%
0.85971234842
 
0.1%
0.95219311791
 
< 0.1%
0.83688285521
 
< 0.1%
0.96429172081
 
< 0.1%
0.84994237371
 
< 0.1%
0.8917708071
 
< 0.1%
0.91208675751
 
< 0.1%
0.87777747751
 
< 0.1%
0.91304076291
 
< 0.1%
Other values (3156)3156
99.6%
ValueCountFrequency (%)
0.73865068621
< 0.1%
0.74756946651
< 0.1%
0.74769486971
< 0.1%
0.7484950081
< 0.1%
0.74867637951
< 0.1%
0.75002262691
< 0.1%
0.76595671561
< 0.1%
0.76603703721
< 0.1%
0.76638464391
< 0.1%
0.76825805291
< 0.1%
ValueCountFrequency (%)
0.9819965891
< 0.1%
0.97848179011
< 0.1%
0.97653297021
< 0.1%
0.97646261951
< 0.1%
0.9763554611
< 0.1%
0.97581392341
< 0.1%
0.97572680211
< 0.1%
0.97538693631
< 0.1%
0.97524614251
< 0.1%
0.97514417111
< 0.1%

sfm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3166
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4082164114
Minimum0.03687647451
Maximum0.8429359314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:57.012532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.03687647451
5-th percentile0.1584453164
Q10.2580405215
median0.3963351568
Q30.5336761646
95-th percentile0.7328248346
Maximum0.8429359314
Range0.8060594569
Interquartile range (IQR)0.2756356431

Descriptive statistics

Standard deviation0.177521105
Coefficient of variation (CV)0.4348700837
Kurtosis-0.8359339024
Mean0.4082164114
Median Absolute Deviation (MAD)0.1380875655
Skewness0.339957584
Sum1293.229591
Variance0.03151374273
MonotonicityNot monotonic
2022-04-13T19:56:57.139531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.31439767972
 
0.1%
0.084934363552
 
0.1%
0.58093854271
 
< 0.1%
0.21970999451
 
< 0.1%
0.75987812151
 
< 0.1%
0.41313985551
 
< 0.1%
0.48953370521
 
< 0.1%
0.38734817211
 
< 0.1%
0.68160320611
 
< 0.1%
0.36960631411
 
< 0.1%
Other values (3156)3156
99.6%
ValueCountFrequency (%)
0.036876474511
< 0.1%
0.080237470841
< 0.1%
0.080963443381
< 0.1%
0.082204085411
< 0.1%
0.082655606351
< 0.1%
0.084934363552
0.1%
0.089739565011
< 0.1%
0.093358554861
< 0.1%
0.094455101791
< 0.1%
0.094675540021
< 0.1%
ValueCountFrequency (%)
0.84293593141
< 0.1%
0.83134686731
< 0.1%
0.82609913851
< 0.1%
0.82267065451
< 0.1%
0.82258663611
< 0.1%
0.82223017831
< 0.1%
0.81805629921
< 0.1%
0.81362951471
< 0.1%
0.81308759541
< 0.1%
0.80844282961
< 0.1%

mode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2825
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1652817968
Minimum0
Maximum0.28
Zeros236
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:57.268584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1180157757
median0.1865986395
Q30.2211041346
95-th percentile0.2608128743
Maximum0.28
Range0.28
Interquartile range (IQR)0.1030883589

Descriptive statistics

Standard deviation0.07720301386
Coefficient of variation (CV)0.4670993139
Kurtosis-0.2559077036
Mean0.1652817968
Median Absolute Deviation (MAD)0.04450988293
Skewness-0.8372359937
Sum523.6127321
Variance0.005960305348
MonotonicityNot monotonic
2022-04-13T19:56:57.386568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0236
 
7.4%
0.2810
 
0.3%
0.060047656875
 
0.2%
0.06005361935
 
0.2%
0.18666666675
 
0.2%
0.050061652284
 
0.1%
0.050087976544
 
0.1%
0.050055865924
 
0.1%
0.050059880244
 
0.1%
0.050114242194
 
0.1%
Other values (2815)2887
91.1%
ValueCountFrequency (%)
0236
7.4%
0.00072790294631
 
< 0.1%
0.00077490774911
 
< 0.1%
0.00080076263111
 
< 0.1%
0.00084273890141
 
< 0.1%
0.00088983050852
 
0.1%
0.0010008936551
 
< 0.1%
0.0013031914891
 
< 0.1%
0.0014129520611
 
< 0.1%
0.0030561330561
 
< 0.1%
ValueCountFrequency (%)
0.2810
0.3%
0.27970338981
 
< 0.1%
0.27958518521
 
< 0.1%
0.27952299831
 
< 0.1%
0.27911811021
 
< 0.1%
0.27887096771
 
< 0.1%
0.27885088921
 
< 0.1%
0.27883333331
 
< 0.1%
0.27861157021
 
< 0.1%
0.27851851851
 
< 0.1%

centroid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3166
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1809066104
Minimum0.03936334258
Maximum0.2511237587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:57.555537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.03936334258
5-th percentile0.1259677733
Q10.1636621363
median0.1848384094
Q30.1991460509
95-th percentile0.2291036805
Maximum0.2511237587
Range0.2117604161
Interquartile range (IQR)0.03548391458

Descriptive statistics

Standard deviation0.0299178379
Coefficient of variation (CV)0.1653772509
Kurtosis0.805160543
Mean0.1809066104
Median Absolute Deviation (MAD)0.01738691301
Skewness-0.617495272
Sum573.1121417
Variance0.0008950770245
MonotonicityNot monotonic
2022-04-13T19:56:57.810532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21218991492
 
0.1%
0.21373237172
 
0.1%
0.22890274791
 
< 0.1%
0.10039941711
 
< 0.1%
0.16015517991
 
< 0.1%
0.23374421481
 
< 0.1%
0.19097060781
 
< 0.1%
0.15660550981
 
< 0.1%
0.13285463091
 
< 0.1%
0.17772273781
 
< 0.1%
Other values (3156)3156
99.6%
ValueCountFrequency (%)
0.039363342581
< 0.1%
0.048254075191
< 0.1%
0.059645486741
< 0.1%
0.059780984961
< 0.1%
0.062182311861
< 0.1%
0.066008740391
< 0.1%
0.074675083071
< 0.1%
0.07570098591
< 0.1%
0.07731550271
< 0.1%
0.078847187171
< 0.1%
ValueCountFrequency (%)
0.25112375871
< 0.1%
0.24963659291
< 0.1%
0.24704068411
< 0.1%
0.24435644691
< 0.1%
0.24352803661
< 0.1%
0.24327214161
< 0.1%
0.24326630321
< 0.1%
0.24263912671
< 0.1%
0.24208161481
< 0.1%
0.24174181061
< 0.1%

meanfun
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3166
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1428067343
Minimum0.05556534931
Maximum0.2376363873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:58.026534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.05556534931
5-th percentile0.09362709442
Q10.1169984856
median0.140518518
Q30.1695806199
95-th percentile0.1934318975
Maximum0.2376363873
Range0.182071038
Interquartile range (IQR)0.05258213425

Descriptive statistics

Standard deviation0.03230443258
Coefficient of variation (CV)0.2262108488
Kurtosis-0.8599596486
Mean0.1428067343
Median Absolute Deviation (MAD)0.02632417047
Skewness0.03914069149
Sum452.4117342
Variance0.001043576364
MonotonicityNot monotonic
2022-04-13T19:56:58.238531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13366730262
 
0.1%
0.13994247832
 
0.1%
0.16507507851
 
< 0.1%
0.11026030261
 
< 0.1%
0.10772472831
 
< 0.1%
0.17723831181
 
< 0.1%
0.15452851161
 
< 0.1%
0.17400932951
 
< 0.1%
0.14329537251
 
< 0.1%
0.18554338451
 
< 0.1%
Other values (3156)3156
99.6%
ValueCountFrequency (%)
0.055565349311
< 0.1%
0.057045239171
< 0.1%
0.060965707491
< 0.1%
0.062541643861
< 0.1%
0.063475179511
< 0.1%
0.064366703291
< 0.1%
0.065827448811
< 0.1%
0.066563908131
< 0.1%
0.067407498731
< 0.1%
0.068517710241
< 0.1%
ValueCountFrequency (%)
0.23763638731
< 0.1%
0.23113528951
< 0.1%
0.229152531
< 0.1%
0.22575547141
< 0.1%
0.22341704861
< 0.1%
0.22089260181
< 0.1%
0.22086281451
< 0.1%
0.2188882851
< 0.1%
0.21725668331
< 0.1%
0.21673890841
< 0.1%

minfun
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct913
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03680180834
Minimum0.009775171065
Maximum0.2040816327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:58.395578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.009775171065
5-th percentile0.0157946693
Q10.01822323462
median0.04610951009
Q30.04790419162
95-th percentile0.0564414737
Maximum0.2040816327
Range0.1943064616
Interquartile range (IQR)0.02968095699

Descriptive statistics

Standard deviation0.01921995215
Coefficient of variation (CV)0.5222556449
Kurtosis10.75808575
Mean0.03680180834
Median Absolute Deviation (MAD)0.01126630451
Skewness1.878003958
Sum116.5881288
Variance0.0003694065605
MonotonicityNot monotonic
2022-04-13T19:56:58.536535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0469208211164
 
2.0%
0.0471050049159
 
1.9%
0.0470127326253
 
1.7%
0.046966731947
 
1.5%
0.0470588235346
 
1.5%
0.0472906403946
 
1.5%
0.0471512770141
 
1.3%
0.0471976401240
 
1.3%
0.047384007940
 
1.3%
0.0474777448135
 
1.1%
Other values (903)2697
85.1%
ValueCountFrequency (%)
0.0097751710651
< 0.1%
0.0097847358121
< 0.1%
0.0099009900991
< 0.1%
0.0099108027751
< 0.1%
0.010162601631
< 0.1%
0.010548523211
< 0.1%
0.010582010582
0.1%
0.010706638121
< 0.1%
0.010775862071
< 0.1%
0.010905125411
< 0.1%
ValueCountFrequency (%)
0.20408163271
< 0.1%
0.22
0.1%
0.18518518521
< 0.1%
0.17857142861
< 0.1%
0.16842105261
< 0.1%
0.162
0.1%
0.15841584161
< 0.1%
0.15384615382
0.1%
0.14678899081
< 0.1%
0.13698630141
< 0.1%

maxfun
Real number (ℝ≥0)

Distinct123
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2588422458
Minimum0.1030927835
Maximum0.2791139241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:58.677534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1030927835
5-th percentile0.1924698795
Q10.253968254
median0.2711864407
Q30.2774566474
95-th percentile0.2790697674
Maximum0.2791139241
Range0.1760211405
Interquartile range (IQR)0.02348839343

Descriptive statistics

Standard deviation0.03007730942
Coefficient of variation (CV)0.1161993837
Kurtosis5.203917878
Mean0.2588422458
Median Absolute Deviation (MAD)0.007883326764
Skewness-2.238534771
Sum820.0122346
Variance0.0009046445422
MonotonicityNot monotonic
2022-04-13T19:56:58.819578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2790697674532
16.8%
0.275862069462
14.6%
0.2774566474339
 
10.7%
0.2711864407235
 
7.4%
0.2666666667159
 
5.0%
0.262295082127
 
4.0%
0.2742857143107
 
3.4%
0.25101
 
3.2%
0.258064516186
 
2.7%
0.25396825462
 
2.0%
Other values (113)958
30.2%
ValueCountFrequency (%)
0.10309278351
< 0.1%
0.10526315791
< 0.1%
0.10869565221
< 0.1%
0.11111111111
< 0.1%
0.11235955061
< 0.1%
0.11363636362
0.1%
0.11494252872
0.1%
0.11764705882
0.1%
0.1190476191
< 0.1%
0.1251
< 0.1%
ValueCountFrequency (%)
0.27911392416
 
0.2%
0.2790697674532
16.8%
0.277777777839
 
1.2%
0.2774566474339
10.7%
0.27735849063
 
0.1%
0.275862069462
14.6%
0.2756251
 
< 0.1%
0.2742857143107
 
3.4%
0.272727272758
 
1.8%
0.2711864407235
7.4%

meandom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2999
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8292109597
Minimum0.0078125
Maximum2.957682292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:58.961596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0078125
5-th percentile0.1044901207
Q10.4198279748
median0.765794837
Q31.177165904
95-th percentile1.800401651
Maximum2.957682292
Range2.949869792
Interquartile range (IQR)0.7573379297

Descriptive statistics

Standard deviation0.5252050333
Coefficient of variation (CV)0.6333792712
Kurtosis-0.05477253025
Mean0.8292109597
Median Absolute Deviation (MAD)0.3749424721
Skewness0.6110224344
Sum2626.94032
Variance0.275840327
MonotonicityNot monotonic
2022-04-13T19:56:59.106580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.007812561
 
1.9%
0.19531254
 
0.1%
0.718754
 
0.1%
0.07031254
 
0.1%
0.46093754
 
0.1%
0.68753
 
0.1%
1.3723958333
 
0.1%
0.36783854173
 
0.1%
1.00781253
 
0.1%
0.25781253
 
0.1%
Other values (2989)3076
97.1%
ValueCountFrequency (%)
0.007812561
1.9%
0.0079787234041
 
< 0.1%
0.0079900568181
 
< 0.1%
0.008184523811
 
< 0.1%
0.0082465277781
 
< 0.1%
0.0090144230771
 
< 0.1%
0.010318396231
 
< 0.1%
0.014772727271
 
< 0.1%
0.016718751
 
< 0.1%
0.016958841461
 
< 0.1%
ValueCountFrequency (%)
2.9576822921
< 0.1%
2.8052455361
< 0.1%
2.6769886361
< 0.1%
2.5915798611
< 0.1%
2.5442708331
< 0.1%
2.5232469511
< 0.1%
2.5201480261
< 0.1%
2.5164741851
< 0.1%
2.5154433141
< 0.1%
2.5141601561
< 0.1%

mindom
Real number (ℝ≥0)

HIGH CORRELATION

Distinct77
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05264704541
Minimum0.0048828125
Maximum0.458984375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:59.239577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0048828125
5-th percentile0.0078125
Q10.0078125
median0.0234375
Q30.0703125
95-th percentile0.1875
Maximum0.458984375
Range0.4541015625
Interquartile range (IQR)0.0625

Descriptive statistics

Standard deviation0.06329947812
Coefficient of variation (CV)1.202336762
Kurtosis2.187585993
Mean0.05264704541
Median Absolute Deviation (MAD)0.015625
Skewness1.661113783
Sum166.7858398
Variance0.004006823931
MonotonicityNot monotonic
2022-04-13T19:56:59.352576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02343751246
39.3%
0.0078125814
25.7%
0.1640625109
 
3.4%
0.054687563
 
2.0%
0.004882812561
 
1.9%
0.0937546
 
1.5%
0.1562546
 
1.5%
0.210937545
 
1.4%
0.070312542
 
1.3%
0.01562538
 
1.2%
Other values (67)658
20.8%
ValueCountFrequency (%)
0.004882812561
 
1.9%
0.0078125814
25.7%
0.01464843752
 
0.1%
0.01562538
 
1.2%
0.019531251
 
< 0.1%
0.021533203139
 
0.3%
0.02343751246
39.3%
0.027343751
 
< 0.1%
0.0292968752
 
0.1%
0.0312534
 
1.1%
ValueCountFrequency (%)
0.4589843751
 
< 0.1%
0.449218751
 
< 0.1%
0.4003906251
 
< 0.1%
0.35156251
 
< 0.1%
0.343751
 
< 0.1%
0.281251
 
< 0.1%
0.25781255
 
0.2%
0.24902343751
 
< 0.1%
0.24218759
 
0.3%
0.23437529
0.9%

maxdom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1054
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.047276738
Minimum0.0078125
Maximum21.8671875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:59.482579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0078125
5-th percentile0.3125
Q12.0703125
median4.9921875
Q37.0078125
95-th percentile10.640625
Maximum21.8671875
Range21.859375
Interquartile range (IQR)4.9375

Descriptive statistics

Standard deviation3.521156612
Coefficient of variation (CV)0.6976349415
Kurtosis1.31473759
Mean5.047276738
Median Absolute Deviation (MAD)2.40625
Skewness0.7261889465
Sum15989.77271
Variance12.39854388
MonotonicityNot monotonic
2022-04-13T19:56:59.619577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.007812561
 
1.9%
721
 
0.7%
5.1562516
 
0.5%
12.023437515
 
0.5%
0.786132812513
 
0.4%
0.562513
 
0.4%
0.695312513
 
0.4%
5.273437511
 
0.3%
0.73437511
 
0.3%
5.39062511
 
0.3%
Other values (1044)2983
94.2%
ValueCountFrequency (%)
0.007812561
1.9%
0.0156253
 
0.1%
0.02343751
 
< 0.1%
0.05468751
 
< 0.1%
0.07031254
 
0.1%
0.11718752
 
0.1%
0.1251
 
< 0.1%
0.13281252
 
0.1%
0.1406252
 
0.1%
0.156251
 
< 0.1%
ValueCountFrequency (%)
21.86718751
< 0.1%
21.843751
< 0.1%
21.7968751
< 0.1%
21.56251
< 0.1%
21.5156251
< 0.1%
20.97656251
< 0.1%
20.8593751
< 0.1%
20.6251
< 0.1%
20.4843751
< 0.1%
20.456542971
< 0.1%

dfrange
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1091
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.994629692
Minimum0
Maximum21.84375
Zeros65
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:56:59.759536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.265625
Q12.044921875
median4.9453125
Q36.9921875
95-th percentile10.60898437
Maximum21.84375
Range21.84375
Interquartile range (IQR)4.947265625

Descriptive statistics

Standard deviation3.52003912
Coefficient of variation (CV)0.7047647847
Kurtosis1.318012674
Mean4.994629692
Median Absolute Deviation (MAD)2.390625
Skewness0.7282610635
Sum15822.98687
Variance12.39067541
MonotonicityNot monotonic
2022-04-13T19:56:59.885578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
065
 
2.1%
5.132812515
 
0.5%
0.62515
 
0.5%
0.679687514
 
0.4%
3.7513
 
0.4%
0.664062512
 
0.4%
0.64062511
 
0.3%
0.70312511
 
0.3%
8.882812511
 
0.3%
610
 
0.3%
Other values (1081)2991
94.4%
ValueCountFrequency (%)
065
2.1%
0.00781253
 
0.1%
0.0156251
 
< 0.1%
0.019531252
 
0.1%
0.02441406251
 
< 0.1%
0.03906252
 
0.1%
0.04394531252
 
0.1%
0.0468752
 
0.1%
0.05371093751
 
< 0.1%
0.05468751
 
< 0.1%
ValueCountFrequency (%)
21.843751
< 0.1%
21.82031251
< 0.1%
21.77343751
< 0.1%
21.53906251
< 0.1%
21.49218751
< 0.1%
20.88281251
< 0.1%
20.83593751
< 0.1%
20.60156251
< 0.1%
20.46093751
< 0.1%
20.3906251
< 0.1%

modindx
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3079
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1737515061
Minimum0
Maximum0.9323741007
Zeros65
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2022-04-13T19:57:00.017538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05774969079
Q10.09976580594
median0.1393570226
Q30.2091832491
95-th percentile0.4055158151
Maximum0.9323741007
Range0.9323741007
Interquartile range (IQR)0.1094174432

Descriptive statistics

Standard deviation0.1194543894
Coefficient of variation (CV)0.6875013176
Kurtosis5.924935217
Mean0.1737515061
Median Absolute Deviation (MAD)0.04741863412
Skewness2.064334578
Sum550.4447715
Variance0.01426935115
MonotonicityNot monotonic
2022-04-13T19:57:00.175533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
065
 
2.1%
0.23
 
0.1%
0.052631578953
 
0.1%
0.13333333333
 
0.1%
0.16666666673
 
0.1%
0.11764705883
 
0.1%
0.14620535712
 
0.1%
0.19230769232
 
0.1%
0.17224880382
 
0.1%
0.052
 
0.1%
Other values (3069)3080
97.2%
ValueCountFrequency (%)
065
2.1%
0.019881353211
 
< 0.1%
0.021647509581
 
< 0.1%
0.021943573671
 
< 0.1%
0.022167487681
 
< 0.1%
0.022486772491
 
< 0.1%
0.024195121951
 
< 0.1%
0.02468354431
 
< 0.1%
0.029226901321
 
< 0.1%
0.029505778211
 
< 0.1%
ValueCountFrequency (%)
0.93237410071
< 0.1%
0.87950310561
< 0.1%
0.85776424531
< 0.1%
0.85470085471
< 0.1%
0.84448275861
< 0.1%
0.84328859061
< 0.1%
0.83344827591
< 0.1%
0.81744186051
< 0.1%
0.79610443431
< 0.1%
0.78076923081
< 0.1%

label
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
female
1584 
male
1584 

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters15840
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female1584
50.0%
male1584
50.0%

Length

2022-04-13T19:57:00.470578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T19:57:00.550585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
female1584
50.0%
male1584
50.0%

Most occurring characters

ValueCountFrequency (%)
e4752
30.0%
m3168
20.0%
a3168
20.0%
l3168
20.0%
f1584
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15840
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4752
30.0%
m3168
20.0%
a3168
20.0%
l3168
20.0%
f1584
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15840
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4752
30.0%
m3168
20.0%
a3168
20.0%
l3168
20.0%
f1584
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4752
30.0%
m3168
20.0%
a3168
20.0%
l3168
20.0%
f1584
 
10.0%

Interactions

2022-04-13T19:56:06.993691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:07.105691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:07.219738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:07.349754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:07.457753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:07.554754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:07.671814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:07.788859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:07.913829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:08.019829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:08.221827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:08.331830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:08.445309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:08.564301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:08.688272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:08.802270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:08.926299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.033257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.140254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.253269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.353268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.455299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.565255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.676258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.784300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.891289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:09.999274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:10.108274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:10.223270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:10.346785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:10.480784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:10.594832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:10.708832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:10.842784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:10.972786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.088784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.216783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.329785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.433784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.538786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.640785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.752788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.860820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:11.981784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.086787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.195784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.305783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.413790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.526783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.627788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.739829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.843830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:12.939836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.055834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.168786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.278786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.382816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.492789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.591784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.696786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.802829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:13.909829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.022788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.133788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.244785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.349788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.452789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.573830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.683785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.788790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:14.898783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.016795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.137821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.258786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.369786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.488786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.599819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.698783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.806788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:15.907785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:16.020788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:16.118786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:16.223785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:16.325786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:16.426788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:16.524837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:16.619836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:16.730788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2022-04-13T19:56:41.289099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:41.423105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:41.540063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:41.655057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:41.779055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:41.907059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.039059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.162053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.264053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.373092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.472103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.572102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.680104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.789099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:42.895101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:43.004101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:43.115824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:43.222870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:43.328823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:43.446825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:43.549826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:43.666840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:43.772823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:44.127826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:44.242872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:44.369826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:44.485871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:44.588870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:44.710869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:44.822828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:44.936825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.042824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.142869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.249826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.353871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.459822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.556869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.651824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.746847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.840825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:45.939861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.053869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.157826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.258860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.365828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.476823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.595871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.705823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.801868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:46.905824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.005870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.103874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.198825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.299823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.402823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.499823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.597872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.701871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.808872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:47.914824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.023828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.136823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.234869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.335870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.450826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.558824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.666856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.783902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.876899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:48.966858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.059859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.151857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.242858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.335858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.436903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.528908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.625902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.715903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.819902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:49.920857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.020855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.139855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.236856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.325860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.438905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.551856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.665858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.768856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.867855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:50.978857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.082859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.184859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.277858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.382858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.484871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.593861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.705564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.813517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:51.925516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.037518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.144517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.253563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.351564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.444518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.544523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.646521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.755520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.854563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:52.956517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:53.057517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-13T19:56:53.172518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-04-13T19:57:00.656546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-13T19:57:00.919534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-13T19:57:01.157579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-13T19:57:01.405600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-13T19:56:53.580521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-13T19:56:53.990516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

meanfreqsdmedianQ25Q75IQRskewkurtsp.entsfmmodecentroidmeanfunminfunmaxfunmeandommindommaxdomdfrangemodindxlabel
00.0597810.0642410.0320270.0150710.0901930.07512212.863462274.4029060.8933690.4919180.0000000.0597810.0842790.0157020.2758620.0078120.0078120.0078120.0000000.000000male
10.0660090.0673100.0402290.0194140.0926660.07325222.423285634.6138550.8921930.5137240.0000000.0660090.1079370.0158260.2500000.0090140.0078120.0546880.0468750.052632male
20.0773160.0838290.0367180.0087010.1319080.12320730.7571551024.9277050.8463890.4789050.0000000.0773160.0987060.0156560.2711860.0079900.0078120.0156250.0078120.046512male
30.1512280.0721110.1580110.0965820.2079550.1113741.2328314.1772960.9633220.7272320.0838780.1512280.0889650.0177980.2500000.2014970.0078120.5625000.5546880.247119male
40.1351200.0791460.1246560.0787200.2060450.1273251.1011744.3337130.9719550.7835680.1042610.1351200.1063980.0169310.2666670.7128120.0078125.4843755.4765620.208274male
50.1327860.0795570.1190900.0679580.2095920.1416341.9325628.3088950.9631810.7383070.1125550.1327860.1101320.0171120.2539680.2982220.0078122.7265622.7187500.125160male
60.1507620.0744630.1601060.0928990.2057180.1128191.5306435.9874980.9675730.7626380.0861970.1507620.1059450.0262300.2666670.4796200.0078125.3125005.3046880.123992male
70.1605140.0767670.1443370.1105320.2319620.1214301.3971564.7666110.9592550.7198580.1283240.1605140.0930520.0177580.1441440.3013390.0078120.5390620.5312500.283937male
80.1422390.0780180.1385870.0882060.2085870.1203811.0997464.0702840.9707230.7709920.2191030.1422390.0967290.0179570.2500000.3364760.0078122.1640622.1562500.148272male
90.1343290.0803500.1214510.0755800.2019570.1263771.1903684.7873100.9752460.8045050.0116990.1343290.1058810.0193000.2622950.3403650.0156254.6953124.6796880.089920male

Last rows

meanfreqsdmedianQ25Q75IQRskewkurtsp.entsfmmodecentroidmeanfunminfunmaxfunmeandommindommaxdomdfrangemodindxlabel
31580.1836670.0406070.1825340.1564800.2076460.0511662.0541387.4830190.8981380.3139250.1770400.1836670.1492370.0186480.2622950.5503120.0078123.4218753.4140620.166503female
31590.1687940.0858420.1889800.0955580.2402290.1446711.4622485.0779560.9562010.7068610.1844420.1687940.1828630.0206990.2711860.9882810.0078125.8828125.8750000.268617female
31600.1517710.0891470.1859700.0581590.2301990.1720401.2277104.3043540.9620450.7445900.2305470.1517710.2016000.0234260.2666670.7667410.0078124.0078124.0000000.192220female
31610.1706560.0812370.1842770.1130120.2390960.1260841.3782565.4316630.9507500.6585580.1615060.1706560.1984750.1600000.2539680.4140620.0078120.7343750.7265620.336918female
31620.1460230.0925250.1834340.0417470.2243370.1825901.3849815.1189270.9489990.6598250.2154820.1460230.1956400.0395060.2758620.5338540.0078122.9921882.9843750.258924female
31630.1318840.0847340.1537070.0492850.2011440.1518591.7621296.6303830.9629340.7631820.2008360.1318840.1827900.0837700.2622950.8328990.0078124.2109384.2031250.161929female
31640.1162210.0892210.0767580.0427180.2049110.1621930.6937302.5039540.9607160.7095700.0136830.1162210.1889800.0344090.2758620.9098560.0390623.6796883.6406250.277897female
31650.1420560.0957980.1837310.0334240.2243600.1909361.8765026.6045090.9468540.6541960.0080060.1420560.2099180.0395060.2758620.4942710.0078122.9375002.9296880.194759female
31660.1436590.0906280.1849760.0435080.2199430.1764351.5910655.3882980.9504360.6754700.2122020.1436590.1723750.0344830.2500000.7913600.0078123.5937503.5859380.311002female
31670.1655090.0928840.1830440.0700720.2508270.1807561.7050295.7691150.9388290.6015290.2677020.1655090.1856070.0622570.2711860.2270220.0078120.5546880.5468750.350000female

Duplicate rows

Most frequently occurring

meanfreqsdmedianQ25Q75IQRskewkurtsp.entsfmmodecentroidmeanfunminfunmaxfunmeandommindommaxdomdfrangemodindxlabel# duplicates
00.2121900.0431900.2151530.1889570.2456440.0566871.8625736.1097900.8776690.3143980.1889570.2121900.1399420.0471980.2790701.9255510.02343815.60937515.5859380.121344female2
10.2137320.0577050.2425730.1417010.2579840.1162832.1135987.8909270.8597120.0849340.2489780.2137320.1336670.0283190.2539680.8181250.1328124.1640624.0312500.229051male2